1. Technical Field
The present disclosure relates to CT angiography ad, more specifically, to computer aided detection of pulmonary embolism with local characteristic features in CT angiography.
2. Discussion of Related Art
A pulmonary embolism (PE) is a blockage such as clot that has traveled through the blood stream to artery of the lung. If untreated, a PE can lead to death in a high proportion of patients. Once detected, PEs may be successfully treated with anticoagulants. Accordingly, the ability to quickly and accurately detect PEs is literally a matter of life and death.
However, proper detection of PEs is particularly challenging and is often prone to errors such as failing to be properly diagnosed. Often, computed tomography (CT) is used to visualize the pulmonary arteries so that a highly trained medical practitioner such as a radiologist can examine the imagery for indications of a PE.
Clinically, manual reading of CT imager is laborious, time consuming and complicated by various PE look-alikes (false positives) including respirator motion artifacts, flow-related artifacts, streak artifacts, partial volume artifacts, stair step artifacts, lymph nodes, and vascular bifurcation, among many others. The accuracy and efficiency of interpreting such a large image data set is also limited by human factors, such as attention span and eye fatigue. Consequently, it is highly desirable to have a computer aided detection (CAD) system to assist radiologists in detecting and characterizing emboli in an accurate, efficient and reproducible way. Such a CAD should achieve a high detection sensitivity with few false positives to acquire clinical acceptance. Moreover, automatic detection systems must be able to rapidly process the CT imagery and detect PE candidates in a timely manner as a patient suffering from a PE must often receive treatment immediately after the first signs of symptoms if treatment is to be successful.
Existing approaches for computer aided diagnosis of PE generally involve vessel segmentation, where prior to the detection of PE candidates, the pulmonary vessel structure is first fully identified from within the CT image data. The search for PE candidates may then be performed within the segmented vessel tree. However, vessel segmentation is computationally expensive and particularly time consuming. Moreover, limiting the search field to the segmented vessel tree may introduce the possibility that errors in segmentation may result in the failure to identify one or more PE candidates. For example, such approaches to computer aided diagnosis of PE candidates may be especially ill-suited for small vasculature where subsegmental PEs often occurs.
Furthermore, where radiocontrast agents are administered, regions of pulmonary vessels where blood is flowing properly are enhanced by the contrast material whereas areas of obstruction may not be fully enhanced. Accordingly, there might not be a need to search for PEs in the enhanced regions of the pulmonary vessel tree. Therefore, even where the entire pulmonary vascular structure is correctly segmented, much of the segmented tree will not be considered for the occurrence of PE candidates.
Existing techniques for image processing include watershed, hierarchical tobogganing, intelligent paint, and intelligent scissor (i.e., “live-wire”). These techniques may represent available imaging processing methods, but have not necessarily been used in the art as part of an approach for PE identification. Watershed techniques include rainfalling simulation and hill climbing. The watershed technique based on hill climbing requires that all the minima be found in advance and marked with distinct labels followed by “hill climbing”. This implies that one would not be able to obtain a watershed region till the whole image has been scanned and processed. “Hierarchical tobogganing” includes repeatedly applying a basic toboggan approach, forming a toboggan hierarchy. “Intelligent paint” is built on top of hierarchical tobogganing to allow the user to interactively “select” the pre-formed toboggan regions at a user pre-specified toboggan hierarchical level, based on cost-ordered region collection.
“Intelligent scissor” or interactive “live-wire” aims to compute an optimal path from a selected seed point to every other points in the image based on unrestricted graph search, so that the user can move the mouse freely in the image plane and interactively “select” a desired path among all the optimal paths based on the current cursor position.
The underlying algorithm in intelligent scissor or interactive live-wire is the Dijkstra's algorithm, which computes a shortest path from a given point to all other points in the image. However, for large images, the underlying graph created in live-wire for search becomes large; the interativeness of livewire may be ineffective due to fundamental limitations of Dijkstra's algorithm. An alternative is toboggan-based live-wire, in which the basic toboggan algorithm is applied to reduce the underlying graph in live-wire to achieve highly efficient interaction in image segmentation.
Moreover, these existing image processing techniques may not be suitable for PE detection as they may fail to extract a toboggan cluster from an initial site without processing pixels beyond its external boundary.
Existing approaches for detection of PE in CT imagery are either computationally inefficient, slow and/or fail to have a sufficient level of accuracy.